Utilities such as water, oil and gas prefer proactive rehabilitation of pipes annually to avoid bursts. The goal is to optimize use of maintenance budget by identifying pipes which are most likely to burst over the next year and fix them to reduce pipe bursts. Because pipe bursts are stochastic, it is important to predict the pipes that are most likely to burst over the next year and fix them in order to reduce the likelihood of pipe bursts.
Utilities typically have a deterministic set of rules that evaluate the risk associated with each pipe and evaluate a risk-reward matrix with the risk (probability of failure) characterized by simple approaches like the inverse of the average life-time of the pipe; and the reward characterized by the damages avoided due to the proactive pipe repair. Low-level physically-based approaches include modeling the pipe degradation by material properties. However, these approaches towards better modeling of individual pipes are not typically scalable and somewhat impractical in the field with uncontrolled environments. Higher-level data-driven approaches model the pipes by learning the probability of failures from past history of bursts. The typical features include static pipe-level metrics such as length, diameter, age, etc., that are independent of other pipes. Existing works tend to ignore dynamic features because the features need to be measured or are difficult to obtain accurately using a well-calibrated hydraulic model. Utilities such as water utilities that do not have generous budgets to do proactive pipe monitoring and maintenance find it very challenging to maintain large pipe networks. Placing a network of sensors for detecting and reacting to bursts in real-time is an ideal solution, however they are expensive to deploy and maintain at scale.